Browse by volume Browse by section Browse by topic Browse by conference

Section: Genomics
Topic: Genetics/Genomics, Microbiology

A primer and discussion on DNA-based microbiome data and related bioinformatics analyses

Douglas, Gavin M.1 ; Langille, Morgan G. I.2  
1 Department of Microbiology and Immunology, McGill University, Montréal, Québec, Canada
2 Department of Pharmacology, Dalhousie University, Halifax, Nova Scotia, Canada

10.24072/pcjournal.2 - Peer Community Journal, Volume 1 (2021), article no. e5.

Get full text PDF Peer reviewed and recommended by PCI

The past decade has seen an eruption of interest in profiling microbiomes through DNA sequencing. The resulting investigations have revealed myriad insights and attracted an influx of researchers to the research area. Many newcomers are in need of primers on the fundamentals of microbiome sequencing data types and the methods used to analyze them. Accordingly, here we aim to provide a detailed, but accessible, introduction to these topics. We first present the background on marker-gene and shotgun metagenomics sequencing and then discuss unique characteristics of microbiome data in general. We highlight several important caveats resulting from these characteristics that should be appreciated when analyzing these data. We then introduce the many-faceted concept of microbial functions and several controversies in this area. One controversy in particular is regarding whether metagenome prediction methods (i.e., based on marker-gene sequences) are sufficiently accurate to ensure reliable biological inferences. We next highlight several underappreciated developments regarding the integration of taxonomic and functional data types. This is a highly pertinent topic because although these data types are inherently connected, they are often analyzed independently and primarily only linked anecdotally in the literature. We close by providing our perspective on this topic in addition to the issue of reproducibility in microbiome research, which are both crucial data analysis challenges facing microbiome researchers.

Published online:
DOI: 10.24072/pcjournal.2
Type: Research article
Douglas, Gavin M. 1; Langille, Morgan G. I. 2

1 Department of Microbiology and Immunology, McGill University, Montréal, Québec, Canada
2 Department of Pharmacology, Dalhousie University, Halifax, Nova Scotia, Canada
License: CC-BY 4.0
Copyrights: The authors retain unrestricted copyrights and publishing rights 
@article{10_24072_pcjournal_2,
     author = {Douglas, Gavin M. and Langille, Morgan G. I.},
     title = {A primer and discussion on {DNA-based} microbiome data and related bioinformatics analyses},
     journal = {Peer Community Journal},
     eid = {e5},
     publisher = {Peer Community In},
     volume = {1},
     year = {2021},
     doi = {10.24072/pcjournal.2},
     url = {https://peercommunityjournal.org/articles/10.24072/pcjournal.2/}
}
TY  - JOUR
AU  - Douglas, Gavin M.
AU  - Langille, Morgan G. I.
TI  - A primer and discussion on DNA-based microbiome data and related bioinformatics analyses
JO  - Peer Community Journal
PY  - 2021
VL  - 1
PB  - Peer Community In
UR  - https://peercommunityjournal.org/articles/10.24072/pcjournal.2/
DO  - 10.24072/pcjournal.2
ID  - 10_24072_pcjournal_2
ER  - 
%0 Journal Article
%A Douglas, Gavin M.
%A Langille, Morgan G. I.
%T A primer and discussion on DNA-based microbiome data and related bioinformatics analyses
%J Peer Community Journal
%D 2021
%V 1
%I Peer Community In
%U https://peercommunityjournal.org/articles/10.24072/pcjournal.2/
%R 10.24072/pcjournal.2
%F 10_24072_pcjournal_2
Douglas, Gavin M.; Langille, Morgan G. I. A primer and discussion on DNA-based microbiome data and related bioinformatics analyses. Peer Community Journal, Volume 1 (2021), article  no. e5. doi : 10.24072/pcjournal.2. https://peercommunityjournal.org/articles/10.24072/pcjournal.2/

Peer reviewed and recommended by PCI : 10.24072/pci.genomics.100008

Conflict of interest of the recommender and peer reviewers:
The recommender in charge of the evaluation of the article and the reviewers declared that they have no conflict of interest (as defined in the code of conduct of PCI) with the authors or with the content of the article.

[1] Abellan-Schneyder, I.; Matchado, M. S.; Reitmeier, S.; Sommer, A.; Sewald, Z.; Baumbach, J.; List, M.; Neuhaus, K. Primer, Pipelines, Parameters: Issues in 16S rRNA Gene Sequencing, mSphere, Volume 6 (2021) no. 1 | DOI

[2] Abu-Ali, G. S.; Mehta, R. S.; Lloyd-Price, J.; Mallick, H.; Branck, T.; Ivey, K. L.; Drew, D. A.; DuLong, C.; Rimm, E.; Izard, J.; Chan, A. T.; Huttenhower, C. Metatranscriptome of human faecal microbial communities in a cohort of adult men, Nature Microbiology, Volume 3 (2018) no. 3, pp. 356-366 | DOI

[3] Abubucker, S.; Segata, N.; Goll, J.; Schubert, A. M.; Izard, J.; Cantarel, B. L.; Rodriguez-Mueller, B.; Zucker, J.; Thiagarajan, M.; Henrissat, B.; White, O.; Kelley, S. T.; Methé, B.; Schloss, P. D.; Gevers, D.; Mitreva, M.; Huttenhower, C. Metabolic Reconstruction for Metagenomic Data and Its Application to the Human Microbiome, PLoS Computational Biology, Volume 8 (2012) no. 6 | DOI

[4] Aitchison, J. The Statistical Analysis of Compositional Data, Journal of the Royal Statistical Society: Series B (Methodological), Volume 44 (1982) no. 2, pp. 139-160 | DOI

[5] Allaband, C.; McDonald, D.; Vázquez-Baeza, Y.; Minich, J. J.; Tripathi, A.; Brenner, D. A.; Loomba, R.; Smarr, L.; Sandborn, W. J.; Schnabl, B.; Dorrestein, P.; Zarrinpar, A.; Knight, R. Microbiome 101: Studying, Analyzing, and Interpreting Gut Microbiome Data for Clinicians, Clinical Gastroenterology and Hepatology, Volume 17 (2019) no. 2, pp. 218-230 | DOI

[6] AMBLER, R. P.; DANIEL, M.; HERMOSO, J.; MEYER, T. E.; BARTSCH, R. G.; KAMEN, M. D. Cytochromes C2 sequence variation among the recognised species of purple nonsulphur photosynthetic bacteria, Nature, Volume 278 (1979) no. 5705, pp. 659-660 | DOI

[7] Amir, A.; McDonald, D.; Navas-Molina, J. A.; Kopylova, E.; Morton, J. T.; Zech Xu, Z.; Kightley, E. P.; Thompson, L. R.; Hyde, E. R.; Gonzalez, A.; Knight, R. Deblur Rapidly Resolves Single-Nucleotide Community Sequence Patterns, mSystems, Volume 2 (2017) no. 2 | DOI

[8] Angly, F. E.; Dennis, P. G.; Skarshewski, A.; Vanwonterghem, I.; Hugenholtz, P.; Tyson, G. W. CopyRighter: a rapid tool for improving the accuracy of microbial community profiles through lineage-specific gene copy number correction, Microbiome, Volume 2 (2014) no. 1 | DOI

[9] Apweiler, R. UniProt: the Universal Protein knowledgebase, Nucleic Acids Research, Volume 32 (2004) no. 90001 | DOI

[10] Arboleya, S.; Bottacini, F.; O’Connell-Motherway, M.; Ryan, C. A.; Ross, R. P.; van Sinderen, D.; Stanton, C. Gene-trait matching across the Bifidobacterium longum pan-genome reveals considerable diversity in carbohydrate catabolism among human infant strains, BMC Genomics, Volume 19 (2018) no. 1 | DOI

[11] Armour, C. R.; Nayfach, S.; Pollard, K. S.; Sharpton, T. J. A Metagenomic Meta-analysis Reveals Functional Signatures of Health and Disease in the Human Gut Microbiome, mSystems, Volume 4 (2019) no. 4 | DOI

[12] Ayling, M.; Clark, M. D.; Leggett, R. M. New approaches for metagenome assembly with short reads, Briefings in Bioinformatics, Volume 21 (2020) no. 2, pp. 584-594 | DOI

[13] Bäckhed, F.; Roswall, J.; Peng, Y.; Feng, Q.; Jia, H.; Kovatcheva-Datchary, P.; Li, Y.; Xia, Y.; Xie, H.; Zhong, H.; Khan, M. T.; Zhang, J.; Li, J.; Xiao, L.; Al-Aama, J.; Zhang, D.; Lee, Y. S.; Kotowska, D.; Colding, C.; Tremaroli, V.; Yin, Y.; Bergman, S.; Xu, X.; Madsen, L.; Kristiansen, K.; Dahlgren, J.; Wang, J. Dynamics and Stabilization of the Human Gut Microbiome during the First Year of Life, Cell Host & Microbe, Volume 17 (2015) no. 5, pp. 690-703 | DOI

[14] Bahram, M.; Anslan, S.; Hildebrand, F.; Bork, P.; Tedersoo, L. Newly designed 16S rRNA metabarcoding primers amplify diverse and novel archaeal taxa from the environment, Environmental Microbiology Reports, Volume 11 (2019) no. 4, pp. 487-494 | DOI

[15] Barott, K. L.; Rodriguez-Mueller, B.; Youle, M.; Marhaver, K. L.; Vermeij, M. J. A.; Smith, J. E.; Rohwer, F. L. Microbial to reef scale interactions between the reef-building coral Montastraea annularis and benthic algae, Proceedings of the Royal Society B: Biological Sciences, Volume 279 (2012) no. 1733, pp. 1655-1664 | DOI

[16] Beiko, R. G.; Harlow, T. J.; Ragan, M. A. Highways of gene sharing in prokaryotes, Proceedings of the National Academy of Sciences, Volume 102 (2005) no. 40, pp. 14332-14337 | DOI

[17] Belton, J.-M.; McCord, R. P.; Gibcus, J. H.; Naumova, N.; Zhan, Y.; Dekker, J. Hi–C: A comprehensive technique to capture the conformation of genomes, Methods, Volume 58 (2012) no. 3, pp. 268-276 | DOI

[18] Berg, G.; Rybakova, D.; Fischer, D.; Cernava, T.; Vergès, M.-C. C.; Charles, T.; Chen, X.; Cocolin, L.; Eversole, K.; Corral, G. H.; Kazou, M.; Kinkel, L.; Lange, L.; Lima, N.; Loy, A.; Macklin, J. A.; Maguin, E.; Mauchline, T.; McClure, R.; Mitter, B.; Ryan, M.; Sarand, I.; Smidt, H.; Schelkle, B.; Roume, H.; Kiran, G. S.; Selvin, J.; Souza, R. S. C. d.; van Overbeek, L.; Singh, B. K.; Wagner, M.; Walsh, A.; Sessitsch, A.; Schloter, M. Microbiome definition re-visited: old concepts and new challenges, Microbiome, Volume 8 (2020) no. 1 | DOI

[19] Bishara, A.; Moss, E. L.; Kolmogorov, M.; Parada, A. E.; Weng, Z.; Sidow, A.; Dekas, A. E.; Batzoglou, S.; Bhatt, A. S. High-quality genome sequences of uncultured microbes by assembly of read clouds, Nature Biotechnology, Volume 36 (2018) no. 11, pp. 1067-1075 | DOI

[20] Bowers, R. M.; Kyrpides, N. C.; Stepanauskas, R.; Harmon-Smith, M.; Doud, D.; Reddy, T. B. K.; Schulz, F.; Jarett, J.; Rivers, A. R.; Eloe-Fadrosh, E. A.; Tringe, S. G.; Ivanova, N. N.; Copeland, A.; Clum, A.; Becraft, E. D.; Malmstrom, R. R.; Birren, B.; Podar, M.; Bork, P.; Weinstock, G. M.; Garrity, G. M.; Dodsworth, J. A.; Yooseph, S.; Sutton, G.; Glöckner, F. O.; Gilbert, J. A.; Nelson, W. C.; Hallam, S. J.; Jungbluth, S. P.; Ettema, T. J. G.; Tighe, S.; Konstantinidis, K. T.; Liu, W.-T.; Baker, B. J.; Rattei, T.; Eisen, J. A.; Hedlund, B.; McMahon, K. D.; Fierer, N.; Knight, R.; Finn, R.; Cochrane, G.; Karsch-Mizrachi, I.; Tyson, G. W.; Rinke, C.; Lapidus, A.; Meyer, F.; Yilmaz, P.; Parks, D. H.; Murat Eren, A.; Schriml, L.; Banfield, J. F.; Hugenholtz, P.; Woyke, T. Minimum information about a single amplified genome (MISAG) and a metagenome-assembled genome (MIMAG) of bacteria and archaea, Nature Biotechnology, Volume 35 (2017) no. 8, pp. 725-731 | DOI

[21] Bowman, J. S.; Ducklow, H. W. Microbial Communities Can Be Described by Metabolic Structure: A General Framework and Application to a Seasonally Variable, Depth-Stratified Microbial Community from the Coastal West Antarctic Peninsula, PLOS ONE, Volume 10 (2015) no. 8 | DOI

[22] Bradley, P. H.; Pollard, K. S. Phylogenize: correcting for phylogeny reveals genes associated with microbial distributions, Bioinformatics, Volume 36 (2020) no. 4, pp. 1289-1290 | DOI

[23] Breiman, L. Machine Learning, 45 (2001) no. 1, pp. 5-32 | DOI

[24] Breitwieser, F. P.; Lu, J.; Salzberg, S. L. A review of methods and databases for metagenomic classification and assembly, Briefings in Bioinformatics, Volume 20 (2019) no. 4, pp. 1125-1136 | DOI

[25] BRENNER, D. J. Deoxyribonucleic Acid Reassociation in the Taxonomy of Enteric Bacteria, International Journal of Systematic Bacteriology, Volume 23 (1973) no. 4, pp. 298-307 | DOI

[26] Buchfink, B.; Xie, C.; Huson, D. H. Fast and sensitive protein alignment using DIAMOND, Nature Methods, Volume 12 (2015) no. 1, pp. 59-60 | DOI

[27] Bukin, Y. S.; Galachyants, Y. P.; Morozov, I. V.; Bukin, S. V.; Zakharenko, A. S.; Zemskaya, T. I. The effect of 16S rRNA region choice on bacterial community metabarcoding results, Scientific Data, Volume 6 (2019) no. 1 | DOI

[28] Burke, C.; Steinberg, P.; Rusch, D.; Kjelleberg, S.; Thomas, T. Bacterial community assembly based on functional genes rather than species, Proceedings of the National Academy of Sciences, Volume 108 (2011) no. 34, pp. 14288-14293 | DOI

[29] Calgaro, M.; Romualdi, C.; Waldron, L.; Risso, D.; Vitulo, N. Assessment of single cell RNA-seq statistical methods on microbiome data, Genome Biology, Volume 21 (2020)

[30] Callahan, B. J.; Sankaran, K.; Fukuyama, J. A.; McMurdie, P. J.; Holmes, S. P. Bioconductor workflow for microbiome data analysis: from raw reads to community analyses, F1000Research, Volume 5 (2016) | DOI

[31] Callahan, B. J.; McMurdie, P. J.; Rosen, M. J.; Han, A. W.; Johnson, A. J. A.; Holmes, S. P. DADA2: High-resolution sample inference from Illumina amplicon data, Nature Methods, Volume 13 (2016) no. 7, pp. 581-583 | DOI

[32] Callahan, B. J.; Wong, J.; Heiner, C.; Oh, S.; Theriot, C. M.; Gulati, A. S.; McGill, S. K.; Dougherty, M. K. High-throughput amplicon sequencing of the full-length 16S rRNA gene with single-nucleotide resolution, Nucleic Acids Research, Volume 47 (2019) no. 18 | DOI

[33] Carini, P.; Marsden, P. J.; Leff, J. W.; Morgan, E. E.; Strickland, M. S.; Fierer, N. Relic DNA is abundant in soil and obscures estimates of soil microbial diversity, Nature Microbiology, Volume 2 (2016) no. 3 | DOI

[34] Case, R. J.; Boucher, Y.; Dahllöf, I.; Holmström, C.; Doolittle, W. F.; Kjelleberg, S. Use of 16S rRNA and rpoB Genes as Molecular Markers for Microbial Ecology Studies, Applied and Environmental Microbiology, Volume 73 (2007) no. 1, pp. 278-288 | DOI

[35] Caspi, R.; Billington, R.; Ferrer, L.; Foerster, H.; Fulcher, C. A.; Keseler, I. M.; Kothari, A.; Krummenacker, M.; Latendresse, M.; Mueller, L. A.; Ong, Q.; Paley, S.; Subhraveti, P.; Weaver, D. S.; Karp, P. D. The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases, Nucleic Acids Research, Volume 44 (2016) no. D1 | DOI

[36] Castaner, O.; Goday, A.; Park, Y.-M.; Lee, S.-H.; Magkos, F.; Shiow, S.-A. T. E.; Schröder, H. The Gut Microbiome Profile in Obesity: A Systematic Review, International Journal of Endocrinology (2018), pp. 1-9 | DOI

[37] Chaffron, S.; Rehrauer, H.; Pernthaler, J.; von Mering, C. A global network of coexisting microbes from environmental and whole-genome sequence data, Genome Research, Volume 20 (2010) no. 7, pp. 947-959 | DOI

[38] Chen, P. E.; Cook, C.; Stewart, A. C.; Nagarajan, N.; Sommer, D. D.; Pop, M.; Thomason, B.; Thomason, M.; Lentz, S.; Nolan, N.; Sozhamannan, S.; Sulakvelidze, A.; Mateczun, A.; Du, L.; Zwick, M. E.; Read, T. D. Genomic characterization of the Yersinia genus, Genome Biology, Volume 11 (2010) no. 1 | DOI

[39] Chen, Z.; Hui, P. C.; Hui, M.; Yeoh, Y. K.; Wong, P. Y.; Chan, M. C. W.; Wong, M. C. S.; Ng, S. C.; Chan, F. K. L.; Chan, P. K. S. Impact of Preservation Method and 16S rRNA Hypervariable Region on Gut Microbiota Profiling, mSystems, Volume 4 (2019) no. 1 | DOI

[40] Comeau, A. M.; Douglas, G. M.; Langille, M. G. I. Microbiome Helper: a Custom and Streamlined Workflow for Microbiome Research, mSystems, Volume 2 (2017) no. 1 | DOI

[41] Cruz, G. N. F.; Christoff, A. P.; de Oliveira, L. F. V. Equivolumetric Protocol Generates Library Sizes Proportional to Total Microbial Load in 16S Amplicon Sequencing, Frontiers in Microbiology, Volume 12 (2021) | DOI

[42] Darling, A. E.; Jospin, G.; Lowe, E.; Matsen, F. A.; Bik, H. M.; Eisen, J. A. PhyloSift: phylogenetic analysis of genomes and metagenomes, PeerJ, Volume 2 (2014) | DOI

[43] Devereux, R.; He, S. H.; Doyle, C. L.; Orkland, S.; Stahl, D. A.; LeGall, J.; Whitman, W. B. Diversity and origin of Desulfovibrio species: phylogenetic definition of a family, Journal of Bacteriology, Volume 172 (1990) no. 7, pp. 3609-3619 | DOI

[44] Douglas, G. M.; Maffei, V. J.; Zaneveld, J. R.; Yurgel, S. N.; Brown, J. R.; Taylor, C. M.; Huttenhower, C.; Langille, M. G. I. PICRUSt2 for prediction of metagenome functions, Nature Biotechnology, Volume 38 (2020) no. 6, pp. 685-688 | DOI

[45] Douglas, G. M.; Langille, M. G. I. Current and Promising Approaches to Identify Horizontal Gene Transfer Events in Metagenomes, Genome Biology and Evolution, Volume 11 (2019) no. 10, pp. 2750-2766 | DOI

[46] Douglas, G. M.; Hansen, R.; Jones, C. M. A.; Dunn, K. A.; Comeau, A. M.; Bielawski, J. P.; Tayler, R.; El-Omar, E. M.; Russell, R. K.; Hold, G. L.; Langille, M. G. I.; Van Limbergen, J. Multi-omics differentially classify disease state and treatment outcome in pediatric Crohn’s disease, Microbiome, Volume 6 (2018) no. 1 | DOI

[47] Drouin, G.; Godin, J.-R.; Page, B. The Genetics of Vitamin C Loss in Vertebrates, Current Genomics, Volume 12 (2011) no. 5, pp. 371-378 | DOI

[48] Duar, R. M.; Lin, X. B.; Zheng, J.; Martino, M. E.; Grenier, T.; Pérez-Muñoz, M. E.; Leulier, F.; Gänzle, M.; Walter, J. Lifestyles in transition: evolution and natural history of the genus Lactobacillus, FEMS Microbiology Reviews, Volume 41 (2017) no. Supp_1 | DOI

[49] Duvallet, C.; Gibbons, S. M.; Gurry, T.; Irizarry, R. A.; Alm, E. J. Meta-analysis of gut microbiome studies identifies disease-specific and shared responses, Nature Communications, Volume 8 (2017) no. 1 | DOI

[50] Edgar, R. C. UNOISE2: improved error-correction for Illumina 16S and ITS amplicon sequencing, bioRxiv (2016)

[51] Pawlowsky-Glahn, V.; Egozcue, J. J. Exploring Compositional Data with the CoDa-Dendrogram, Austrian Journal of Statistics, Volume 40 (2011) | DOI

[52] Eng, A.; Borenstein, E. Taxa-function robustness in microbial communities, Microbiome, Volume 6 (2018) no. 1 | DOI

[53] Farrelly, V.; Rainey, F. A.; Stackebrandt, E. Effect of genome size and rrn gene copy number on PCR amplification of 16S rRNA genes from a mixture of bacterial species, Applied and Environmental Microbiology, Volume 61 (1995) no. 7, pp. 2798-2801 | DOI

[54] Faust, K.; Sathirapongsasuti, J. F.; Izard, J.; Segata, N.; Gevers, D.; Raes, J.; Huttenhower, C. Microbial Co-occurrence Relationships in the Human Microbiome, PLoS Computational Biology, Volume 8 (2012) no. 7 | DOI

[55] Fernandes, A. D.; Macklaim, J. M.; Linn, T. G.; Reid, G.; Gloor, G. B. ANOVA-Like Differential Expression (ALDEx) Analysis for Mixed Population RNA-Seq, PLoS ONE, Volume 8 (2014) no. 7 | DOI

[56] Finn, R. D.; Bateman, A.; Clements, J.; Coggill, P.; Eberhardt, R. Y.; Eddy, S. R.; Heger, A.; Hetherington, K.; Holm, L.; Mistry, J.; Sonnhammer, E. L. L.; Tate, J.; Punta, M. Pfam: the protein families database, Nucleic Acids Research, Volume 42 (2014) no. D1 | DOI

[57] Fitch, W. M.; Margoliash, E. Construction of Phylogenetic Trees, Science, Volume 155 (1967) no. 3760, pp. 279-284 | DOI

[58] Fox, G. E.; Wisotzkey, J. D.; Jurtshuk, P. How Close Is Close: 16S rRNA Sequence Identity May Not Be Sufficient To Guarantee Species Identity, International Journal of Systematic Bacteriology, Volume 42 (1992) no. 1, pp. 166-170 | DOI

[59] Francke, C.; Siezen, R. J.; Teusink, B. Reconstructing the metabolic network of a bacterium from its genome, Trends in Microbiology, Volume 13 (2005) no. 11, pp. 550-558 | DOI

[60] Franzosa, E. A.; McIver, L. J.; Rahnavard, G.; Thompson, L. R.; Schirmer, M.; Weingart, G.; Lipson, K. S.; Knight, R.; Caporaso, J. G.; Segata, N.; Huttenhower, C. Species-level functional profiling of metagenomes and metatranscriptomes, Nature Methods, Volume 15 (2018) no. 11, pp. 962-968 | DOI

[61] Friedman, J.; Alm, E. J. Inferring Correlation Networks from Genomic Survey Data, PLoS Computational Biology, Volume 8 (2012) no. 9 | DOI

[62] Frioux, C.; Singh, D.; Korcsmaros, T.; Hildebrand, F. From bag-of-genes to bag-of-genomes: metabolic modelling of communities in the era of metagenome-assembled genomes, Computational and Structural Biotechnology Journal, Volume 18 (2020), pp. 1722-1734 | DOI

[63] Galperin, M. Y.; Makarova, K. S.; Wolf, Y. I.; Koonin, E. V. Expanded microbial genome coverage and improved protein family annotation in the COG database, Nucleic Acids Research, Volume 43 (2015) no. D1 | DOI

[64] Galperin, M. Y.; Kristensen, D. M.; Makarova, K. S.; Wolf, Y. I.; Koonin, E. V. Microbial genome analysis: the COG approach, Briefings in Bioinformatics, Volume 20 (2019) no. 4, pp. 1063-1070 | DOI

[65] Galushko, A.; Kuever, J. D esulfobacter, Bergey's Manual of Systematics of Archaea and Bacteria, 2019, pp. 1-8 | DOI

[66] Gevers, D.; Kugathasan, S.; Denson, L. A.; Vázquez-Baeza, Y.; Van Treuren, W.; Ren, B.; Schwager, E.; Knights, D.; Song, S. J.; Yassour, M.; Morgan, X. C.; Kostic, A. D.; Luo, C.; González, A.; McDonald, D.; Haberman, Y.; Walters, T.; Baker, S.; Rosh, J.; Stephens, M.; Heyman, M.; Markowitz, J.; Baldassano, R.; Griffiths, A.; Sylvester, F.; Mack, D.; Kim, S.; Crandall, W.; Hyams, J.; Huttenhower, C.; Knight, R.; Xavier, R. J. The Treatment-Naive Microbiome in New-Onset Crohn’s Disease, Cell Host & Microbe, Volume 15 (2014) no. 3, pp. 382-392 | DOI

[67] Gloor, G. B.; Wu, J. R.; Pawlowsky-Glahn, V.; Egozcue, J. J. It's all relative: analyzing microbiome data as compositions, Annals of Epidemiology, Volume 26 (2016) no. 5, pp. 322-329 | DOI

[68] Gloor, G. B.; Macklaim, J. M.; Pawlowsky-Glahn, V.; Egozcue, J. J. Microbiome Datasets Are Compositional: And This Is Not Optional, Frontiers in Microbiology, Volume 8 (2017) | DOI

[69] Goodrich, J. K.; Di Rienzi, S. C.; Poole, A. C.; Koren, O.; Walters, W. A.; Caporaso, J. G.; Knight, R.; Ley, R. E. Conducting a Microbiome Study, Cell, Volume 158 (2014) no. 2, pp. 250-262 | DOI

[70] Graspeuntner, S.; Loeper, N.; Künzel, S.; Baines, J. F.; Rupp, J. Selection of validated hypervariable regions is crucial in 16S-based microbiota studies of the female genital tract, Scientific Reports, Volume 8 (2018) no. 1 | DOI

[71] Greathouse, K. L.; Sinha, R.; Vogtmann, E. DNA extraction for human microbiome studies: the issue of standardization, Genome Biology, Volume 20 (2019) no. 1 | DOI

[72] Greenblum, S.; Chiu, H.-C.; Levy, R.; Carr, R.; Borenstein, E. Towards a predictive systems-level model of the human microbiome: progress, challenges, and opportunities, Current Opinion in Biotechnology, Volume 24 (2013) no. 4, pp. 810-820 | DOI

[73] Gruber-Vodicka, H. R.; Seah, B. K. B.; Pruesse, E. phyloFlash: Rapid Small-Subunit rRNA Profiling and Targeted Assembly from Metagenomes, mSystems, Volume 5 (2020) no. 5 | DOI

[74] Guo, F.; Zhang, T. Biases during DNA extraction of activated sludge samples revealed by high throughput sequencing, Applied Microbiology and Biotechnology, Volume 97 (2013) no. 10, pp. 4607-4616 | DOI

[75] Haft, D. H. The TIGRFAMs database of protein families, Nucleic Acids Research, Volume 31 (2003) no. 1, pp. 371-373 | DOI

[76] Hallam, S. J.; Girguis, P. R.; Preston, C. M.; Richardson, P. M.; DeLong, E. F. Identification of Methyl Coenzyme M Reductase A ( mcrA ) Genes Associated with Methane-Oxidizing Archaea, Applied and Environmental Microbiology, Volume 69 (2003) no. 9, pp. 5483-5491 | DOI

[77] Hao, X.; Chen, T. OTU Analysis Using Metagenomic Shotgun Sequencing Data, PLoS ONE, Volume 7 (2012) no. 11 | DOI

[78] Hastie, A. R.; Dong, L.; Smith, A.; Finklestein, J.; Lam, E. T.; Huo, N.; Cao, H.; Kwok, P.-Y.; Deal, K. R.; Dvorak, J.; Luo, M.-C.; Gu, Y.; Xiao, M. Rapid Genome Mapping in Nanochannel Arrays for Highly Complete and Accurate De Novo Sequence Assembly of the Complex Aegilops tauschii Genome, PLoS ONE, Volume 8 (2013) no. 2 | DOI

[79] HAUBEN, L.; VAUTERIN, L.; SWINGS, J.; MOORE, E. R. B. Comparison of 16S Ribosomal DNA Sequences of All Xanthomonas Species, International Journal of Systematic Bacteriology, Volume 47 (1997) no. 2, pp. 328-335 | DOI

[80] Hauben, L.; Vauterin, L.; Moore, E. R. B.; Hoste, B.; Swings, J. Genomic diversity of the genus Stenotrophomonas, International Journal of Systematic and Evolutionary Microbiology, Volume 49 (1999) no. 4, pp. 1749-1760 | DOI

[81] Hawinkel, S.; Mattiello, F.; Bijnens, L.; Thas, O. A broken promise: microbiome differential abundance methods do not control the false discovery rate, Briefings in Bioinformatics, Volume 20 (2019) no. 1, pp. 210-221 | DOI

[82] Hill, C. You have the microbiome you deserve, Gut Microbiome, Volume 1 (2020) | DOI

[83] Hillmann, B.; Al-Ghalith, G. A.; Shields-Cutler, R. R.; Zhu, Q.; Gohl, D. M.; Beckman, K. B.; Knight, R.; Knights, D. Evaluating the Information Content of Shallow Shotgun Metagenomics, mSystems, Volume 3 (2018) no. 6 | DOI

[84] The Human Microbiome Project Consortium Structure, function and diversity of the healthy human microbiome, Nature, Volume 486 (2013) no. 7402, pp. 207-214 | DOI

[85] Hug, L. A.; Edwards, E. A. Diversity of reductive dehalogenase genes from environmental samples and enrichment cultures identified with degenerate primer PCR screens, Frontiers in Microbiology, Volume 4 (2013) | DOI

[86] Huson, D. H.; Auch, A. F.; Qi, J.; Schuster, S. C. MEGAN analysis of metagenomic data, Genome Research, Volume 17 (2007) no. 3, pp. 377-386 | DOI

[87] Ibrahim, A.; Goebel, B.; Liesack, W.; Griffiths, M.; Stackebrandt, E. The phylogeny of the genusYersiniabased on 16S rDNA sequences, FEMS Microbiology Letters, Volume 114 (1993) no. 2, pp. 173-177 | DOI

[88] Inkpen, S. A.; Douglas, G. M.; Brunet, T. D. P.; Leuschen, K.; Doolittle, W. F.; Langille, M. G. I. The coupling of taxonomy and function in microbiomes, Biology & Philosophy, Volume 32 (2017) no. 6, pp. 1225-1243 | DOI

[89] Iwai, S.; Weinmaier, T.; Schmidt, B. L.; Albertson, D. G.; Poloso, N. J.; Dabbagh, K.; DeSantis, T. Z. Piphillin: Improved Prediction of Metagenomic Content by Direct Inference from Human Microbiomes, PLOS ONE, Volume 11 (2016) no. 11 | DOI

[90] Jackson, D. A. COMPOSITIONAL DATA IN COMMUNITY ECOLOGY: THE PARADIGM OR PERIL OF PROPORTIONS?, Ecology, Volume 78 (1997) no. 3, pp. 929-940 | DOI

[91] Janda, J. M.; Abbott, S. L. 16S rRNA Gene Sequencing for Bacterial Identification in the Diagnostic Laboratory: Pluses, Perils, and Pitfalls, Journal of Clinical Microbiology, Volume 45 (2007) no. 9, pp. 2761-2764 | DOI

[92] Paramel Jayaprakash, T.; Schellenberg, J. J.; Hill, J. E. Resolution and Characterization of Distinct cpn60-Based Subgroups of Gardnerella vaginalis in the Vaginal Microbiota, PLoS ONE, Volume 7 (2012) no. 8 | DOI

[93] Jensen, L. J.; Julien, P.; Kuhn, M.; von Mering, C.; Muller, J.; Doerks, T.; Bork, P. eggNOG: automated construction and annotation of orthologous groups of genes, Nucleic Acids Research, Volume 36 (2008) no. Database | DOI

[94] Johnson, J. S.; Spakowicz, D. J.; Hong, B.-Y.; Petersen, L. M.; Demkowicz, P.; Chen, L.; Leopold, S. R.; Hanson, B. M.; Agresta, H. O.; Gerstein, M.; Sodergren, E.; Weinstock, G. M. Evaluation of 16S rRNA gene sequencing for species and strain-level microbiome analysis, Nature Communications, Volume 10 (2019) no. 1 | DOI

[95] Jones, M. B.; Highlander, S. K.; Anderson, E. L.; Li, W.; Dayrit, M.; Klitgord, N.; Fabani, M. M.; Seguritan, V.; Green, J.; Pride, D. T.; Yooseph, S.; Biggs, W.; Nelson, K. E.; Venter, J. C. Library preparation methodology can influence genomic and functional predictions in human microbiome research, Proceedings of the National Academy of Sciences, Volume 112 (2015) no. 45, pp. 14024-14029 | DOI

[96] Jost, L. Entropy and diversity, Oikos, Volume 113 (2006) no. 2, pp. 363-375 | DOI

[97] Jun, S.-R.; Robeson, M. S.; Hauser, L. J.; Schadt, C. W.; Gorin, A. A. PanFP: pangenome-based functional profiles for microbial communities, BMC Research Notes, Volume 8 (2015) no. 1 | DOI

[98] Kalan, L. R.; Meisel, J. S.; Loesche, M. A.; Horwinski, J.; Soaita, I.; Chen, X.; Uberoi, A.; Gardner, S. E.; Grice, E. A. Strain- and Species-Level Variation in the Microbiome of Diabetic Wounds Is Associated with Clinical Outcomes and Therapeutic Efficacy, Cell Host & Microbe, Volume 25 (2019) no. 5 | DOI

[99] Kallonen, T.; Brodrick, H. J.; Harris, S. R.; Corander, J.; Brown, N. M.; Martin, V.; Peacock, S. J.; Parkhill, J. Systematic longitudinal survey of invasive Escherichia coli in England demonstrates a stable population structure only transiently disturbed by the emergence of ST131, Genome Research, Volume 27 (2017) no. 8, pp. 1437-1449 | DOI

[100] Ogata, H.; Goto, S.; Sato, K.; Fujibuchi, W.; Bono, H.; Kanehisa, M. KEGG: Kyoto Encyclopedia of Genes and Genomes, Nucleic Acids Research, Volume 27 (1999) no. 1, pp. 29-34 | DOI

[101] Kanehisa, M.; Sato, Y.; Kawashima, M.; Furumichi, M.; Tanabe, M. KEGG as a reference resource for gene and protein annotation, Nucleic Acids Research, Volume 44 (2016) no. D1 | DOI

[102] Kang, C.-H.; Nam, Y.-D.; Chung, W.-H.; Quan, Z.-X.; Park, Y.-H.; Park, S.-J.; Desmone, R.; Wan, X.-F.; Rhee, S.-K. Relationship between genome similarity and DNA-DNA hybridization among closely related bacteria, J Microbiol Biotechnol, Volume 17 (2007)

[103] Keinan, A.; Sandbank, B.; Hilgetag, C. C.; Meilijson, I.; Ruppin, E. Fair Attribution of Functional Contribution in Artificial and Biological Networks, Neural Computation, Volume 16 (2004) no. 9, pp. 1887-1915 | DOI

[104] Keswani, J.; Whitman, W. B. Relationship of 16S rRNA sequence similarity to DNA hybridization in prokaryotes., International Journal of Systematic and Evolutionary Microbiology, Volume 51 (2001) no. 2, pp. 667-678 | DOI

[105] Kim, D.; Song, L.; Breitwieser, F. P.; Salzberg, S. L. Centrifuge: rapid and sensitive classification of metagenomic sequences, Genome Research, Volume 26 (2016) no. 12, pp. 1721-1729 | DOI

[106] Kloesges, T.; Popa, O.; Martin, W.; Dagan, T. Networks of Gene Sharing among 329 Proteobacterial Genomes Reveal Differences in Lateral Gene Transfer Frequency at Different Phylogenetic Depths, Molecular Biology and Evolution, Volume 28 (2011) no. 2, pp. 1057-1074 | DOI

[107] Knight, R.; Vrbanac, A.; Taylor, B. C.; Aksenov, A.; Callewaert, C.; Debelius, J.; Gonzalez, A.; Kosciolek, T.; McCall, L.-I.; McDonald, D.; Melnik, A. V.; Morton, J. T.; Navas, J.; Quinn, R. A.; Sanders, J. G.; Swafford, A. D.; Thompson, L. R.; Tripathi, A.; Xu, Z. Z.; Zaneveld, J. R.; Zhu, Q.; Caporaso, J. G.; Dorrestein, P. C. Best practices for analysing microbiomes, Nature Reviews Microbiology, Volume 16 (2018) no. 7, pp. 410-422 | DOI

[108] Knights, D.; Parfrey, L. W.; Zaneveld, J.; Lozupone, C.; Knight, R. Human-Associated Microbial Signatures: Examining Their Predictive Value, Cell Host & Microbe, Volume 10 (2011) no. 4, pp. 292-296 | DOI

[109] Konstantinidis, K. T.; Tiedje, J. M. Genomic insights that advance the species definition for prokaryotes, Proceedings of the National Academy of Sciences, Volume 102 (2005) no. 7, pp. 2567-2572 | DOI

[110] Koonin, E. V.; Galperin, M. Y. Sequence - Evolution - Function: Computational Approaches in Comparative Genomics, Kluwer Academic, Boston, 2003

[111] Kurtz, Z. D.; Müller, C. L.; Miraldi, E. R.; Littman, D. R.; Blaser, M. J.; Bonneau, R. A. Sparse and Compositionally Robust Inference of Microbial Ecological Networks, PLOS Computational Biology, Volume 11 (2015) no. 5 | DOI

[112] Langille, M. G. I.; Zaneveld, J.; Caporaso, J. G.; McDonald, D.; Knights, D.; Reyes, J. A.; Clemente, J. C.; Burkepile, D. E.; Vega Thurber, R. L.; Knight, R.; Beiko, R. G.; Huttenhower, C. Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences, Nature Biotechnology, Volume 31 (2013) no. 9, pp. 814-821 | DOI

[113] Laserna-Mendieta, E. J.; Clooney, A. G.; Carretero-Gomez, J. F.; Moran, C.; Sheehan, D.; Nolan, J. A.; Hill, C.; Gahan, C. G. M.; Joyce, S. A.; Shanahan, F.; Claesson, M. J. Determinants of Reduced Genetic Capacity for Butyrate Synthesis by the Gut Microbiome in Crohn’s Disease and Ulcerative Colitis, Journal of Crohn's and Colitis, Volume 12 (2018) no. 2, pp. 204-216 | DOI

[114] Lau, J. T.; Whelan, F. J.; Herath, I.; Lee, C. H.; Collins, S. M.; Bercik, P.; Surette, M. G. Capturing the diversity of the human gut microbiota through culture-enriched molecular profiling, Genome Medicine, Volume 8 (2016) no. 1 | DOI

[115] Ley, R. E.; Peterson, D. A.; Gordon, J. I. Ecological and Evolutionary Forces Shaping Microbial Diversity in the Human Intestine, Cell, Volume 124 (2006) no. 4, pp. 837-848 | DOI

[116] Li, D.; Liu, C.-M.; Luo, R.; Sadakane, K.; Lam, T.-W. MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph, Bioinformatics, Volume 31 (2015) no. 10, pp. 1674-1676 | DOI

[117] Links, M. G.; Dumonceaux, T. J.; Hemmingsen, S. M.; Hill, J. E. The Chaperonin-60 Universal Target Is a Barcode for Bacteria That Enables De Novo Assembly of Metagenomic Sequence Data, PLoS ONE, Volume 7 (2012) no. 11 | DOI

[118] Liu, J.; Yu, Y.; Cai, Z.; Bartlam, M.; Wang, Y. Comparison of ITS and 18S rDNA for estimating fungal diversity using PCR–DGGE, World Journal of Microbiology and Biotechnology, Volume 31 (2015) no. 9, pp. 1387-1395 | DOI

[119] Lloyd-Price, J.; Mahurkar, A.; Rahnavard, G.; Crabtree, J.; Orvis, J.; Hall, A. B.; Brady, A.; Creasy, H. H.; McCracken, C.; Giglio, M. G.; McDonald, D.; Franzosa, E. A.; Knight, R.; White, O.; Huttenhower, C. Erratum: Strains, functions and dynamics in the expanded Human Microbiome Project, Nature, Volume 551 (2017) no. 7679, p. 256-256 | DOI

[120] Louca, S.; Doebeli, M. Taxonomic variability and functional stability in microbial communities infected by phages, Environmental Microbiology, Volume 19 (2017) no. 10, pp. 3863-3878 | DOI

[121] Louca, S.; Doebeli, M.; Parfrey, L. W. Correcting for 16S rRNA gene copy numbers in microbiome surveys remains an unsolved problem, Microbiome, Volume 6 (2018) no. 1 | DOI

[122] Louca, S.; Parfrey, L. W.; Doebeli, M. Decoupling function and taxonomy in the global ocean microbiome, Science, Volume 353 (2016) no. 6305, pp. 1272-1277 | DOI

[123] Love, M. I.; Huber, W.; Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2, Genome Biology, Volume 15 (2014) no. 12 | DOI

[124] Lozupone, C.; Knight, R. UniFrac: a New Phylogenetic Method for Comparing Microbial Communities, Applied and Environmental Microbiology, Volume 71 (2005) no. 12, pp. 8228-8235 | DOI

[125] Lozupone, C. A.; Hamady, M.; Cantarel, B. L.; Coutinho, P. M.; Henrissat, B.; Gordon, J. I.; Knight, R. The convergence of carbohydrate active gene repertoires in human gut microbes, Proceedings of the National Academy of Sciences, Volume 105 (2008) no. 39, pp. 15076-15081 | DOI

[126] Lu, J.; Breitwieser, F. P.; Thielen, P.; Salzberg, S. L. Bracken: estimating species abundance in metagenomics data, PeerJ Computer Science, Volume 3 (2017) | DOI

[127] Maghini, D. G.; Moss, E. L.; Vance, S. E.; Bhatt, A. S. Improved high-molecular-weight DNA extraction, nanopore sequencing and metagenomic assembly from the human gut microbiome, Nature Protocols, Volume 16 (2020) no. 1, pp. 458-471 | DOI

[128] Makarova, K.; Wolf, Y.; Koonin, E. Archaeal Clusters of Orthologous Genes (arCOGs): An Update and Application for Analysis of Shared Features between Thermococcales, Methanococcales, and Methanobacteriales, Life, Volume 5 (2015) no. 1, pp. 818-840 | DOI

[129] Mandal, S.; Van Treuren, W.; White, R. A.; Eggesbø, M.; Knight, R.; Peddada, S. D. Analysis of composition of microbiomes: a novel method for studying microbial composition, Microbial Ecology in Health & Disease, Volume 26 (2015) no. 0 | DOI

[130] Meyer, S. A.; Phaff, H. J. Deoxyribonucleic Acid Base Composition in Yeasts, Journal of Bacteriology, Volume 97 (2021) no. 1, pp. 52-56 | DOI

[131] Manor, O.; Borenstein, E. Revised computational metagenomic processing uncovers hidden and biologically meaningful functional variation in the human microbiome, Microbiome, Volume 5 (2017) no. 1 | DOI

[132] Manor, O.; Borenstein, E. Systematic Characterization and Analysis of the Taxonomic Drivers of Functional Shifts in the Human Microbiome, Cell Host & Microbe, Volume 21 (2017) no. 2, pp. 254-267 | DOI

[133] Martin, B. D.; Witten, D.; Willis, A. D. Modeling microbial abundances and dysbiosis with beta-binomial regression, The Annals of Applied Statistics, Volume 14 (2020) no. 1 | DOI

[134] Martiny, A. C.; Treseder, K.; Pusch, G. Phylogenetic conservatism of functional traits in microorganisms, The ISME Journal, Volume 7 (2013) no. 4, pp. 830-838 | DOI

[135] Martiny, A. C. High proportions of bacteria are culturable across major biomes, The ISME Journal, Volume 13 (2019) no. 8, pp. 2125-2128 | DOI

[136] Maukonen, J.; Simões, C.; Saarela, M. The currently used commercial DNA-extraction methods give different results of clostridial and actinobacterial populations derived from human fecal samples, FEMS Microbiology Ecology, Volume 79 (2012) no. 3, pp. 697-708 | DOI

[137] McCarthy, A. Third Generation DNA Sequencing: Pacific Biosciences' Single Molecule Real Time Technology, Chemistry & Biology, Volume 17 (2010) no. 7, pp. 675-676 | DOI

[138] McIntyre, A. B. R.; Ounit, R.; Afshinnekoo, E.; Prill, R. J.; Hénaff, E.; Alexander, N.; Minot, S. S.; Danko, D.; Foox, J.; Ahsanuddin, S.; Tighe, S.; Hasan, N. A.; Subramanian, P.; Moffat, K.; Levy, S.; Lonardi, S.; Greenfield, N.; Colwell, R. R.; Rosen, G. L.; Mason, C. E. Comprehensive benchmarking and ensemble approaches for metagenomic classifiers, Genome Biology, Volume 18 (2017) no. 1 | DOI

[139] McLaren, M. R.; Willis, A. D.; Callahan, B. J. Consistent and correctable bias in metagenomic sequencing experiments, eLife, Volume 8 (2019) | DOI

[140] McMahon, K. ‘Metagenomics 2.0’, Environmental Microbiology Reports, Volume 7 (2015) no. 1, pp. 38-39 | DOI

[141] McNally, C. P.; Eng, A.; Noecker, C.; Gagne-Maynard, W. C.; Borenstein, E. BURRITO: An Interactive Multi-Omic Tool for Visualizing Taxa–Function Relationships in Microbiome Data, Frontiers in Microbiology, Volume 9 (2018) | DOI

[142] Menzel, P.; Ng, K. L.; Krogh, A. Fast and sensitive taxonomic classification for metagenomics with Kaiju, Nature Communications, Volume 7 (2016) no. 1 | DOI

[143] Meyer, F.; Paarmann, D.; D'Souza, M.; Olson, R.; Glass, E.; Kubal, M.; Paczian, T.; Rodriguez, A.; Stevens, R.; Wilke, A.; Wilkening, J.; Edwards, R. The metagenomics RAST server – a public resource for the automatic phylogenetic and functional analysis of metagenomes, BMC Bioinformatics, Volume 9 (2008) no. 1 | DOI

[144] Meyer, F.; Overbeek, R.; Rodriguez, A. FIGfams: yet another set of protein families, Nucleic Acids Research, Volume 37 (2009) no. 20, pp. 6643-6654 | DOI

[145] Mikheyev, A. S.; Tin, M. M. Y. A first look at the Oxford Nanopore MinION sequencer, Molecular Ecology Resources, Volume 14 (2014) no. 6, pp. 1097-1102 | DOI

[146] Miller, C. S.; Baker, B. J.; Thomas, B. C.; Singer, S. W.; Banfield, J. F. EMIRGE: reconstruction of full-length ribosomal genes from microbial community short read sequencing data, Genome Biology, Volume 12 (2011) no. 5 | DOI

[147] Miossec, M. J.; Valenzuela, S. L.; Pérez-Losada, M.; Johnson, W. E.; Crandall, K. A.; Castro-Nallar, E. Evaluation of computational methods for human microbiome analysis using simulated data, PeerJ, Volume 8 (2020) | DOI

[148] Morgan, X. C.; Tickle, T. L.; Sokol, H.; Gevers, D.; Devaney, K. L.; Ward, D. V.; Reyes, J. A.; Shah, S. A.; LeLeiko, N.; Snapper, S. B.; Bousvaros, A.; Korzenik, J.; Sands, B. E.; Xavier, R. J.; Huttenhower, C. Dysfunction of the intestinal microbiome in inflammatory bowel disease and treatment, Genome Biology, Volume 13 (2012) no. 9 | DOI

[149] Moriya, Y.; Itoh, M.; Okuda, S.; Yoshizawa, A. C.; Kanehisa, M. KAAS: an automatic genome annotation and pathway reconstruction server, Nucleic Acids Research, Volume 35 (2007) no. Web Server | DOI

[150] Morris, J. J.; Lenski, R. E.; Zinser, E. R. The Black Queen Hypothesis: Evolution of Dependencies through Adaptive Gene Loss, mBio, Volume 3 (2012) no. 2 | DOI

[151] Morton, J. T.; Sanders, J.; Quinn, R. A.; McDonald, D.; Gonzalez, A.; Vázquez-Baeza, Y.; Navas-Molina, J. A.; Song, S. J.; Metcalf, J. L.; Hyde, E. R.; Lladser, M.; Dorrestein, P. C.; Knight, R. Balance Trees Reveal Microbial Niche Differentiation, mSystems, Volume 2 (2017) no. 1 | DOI

[152] Morton, J. T.; Marotz, C.; Washburne, A.; Silverman, J.; Zaramela, L. S.; Edlund, A.; Zengler, K.; Knight, R. Establishing microbial composition measurement standards with reference frames, Nature Communications, Volume 10 (2019) no. 1 | DOI

[153] Mosca, A.; Leclerc, M.; Hugot, J. P. Gut Microbiota Diversity and Human Diseases: Should We Reintroduce Key Predators in Our Ecosystem?, Frontiers in Microbiology, Volume 7 (2016) | DOI

[154] Moya, A.; Ferrer, M. Functional Redundancy-Induced Stability of Gut Microbiota Subjected to Disturbance, Trends in Microbiology, Volume 24 (2016) no. 5, pp. 402-413 | DOI

[155] Muegge, B. D.; Kuczynski, J.; Knights, D.; Clemente, J. C.; González, A.; Fontana, L.; Henrissat, B.; Knight, R.; Gordon, J. I. Diet Drives Convergence in Gut Microbiome Functions Across Mammalian Phylogeny and Within Humans, Science, Volume 332 (2011) no. 6032, pp. 970-974 | DOI

[156] Muscogiuri, G.; Cantone, E.; Cassarano, S.; Tuccinardi, D.; Barrea, L.; Savastano, S.; Colao, A. Gut microbiota: a new path to treat obesity, International Journal of Obesity Supplements, Volume 9 (2019) no. 1, pp. 10-19 | DOI

[157] Mysara, M.; Vandamme, P.; Props, R.; Kerckhof, F.-M.; Leys, N.; Boon, N.; Raes, J.; Monsieurs, P. Reconciliation between operational taxonomic units and species boundaries, FEMS Microbiology Ecology, Volume 93 (2017) no. 4 | DOI

[158] Naeem, S.; Kawabata, Z.; Loreau, M. Transcending boundaries in biodiversity research, Trends in Ecology & Evolution, Volume 13 (1998) no. 4, pp. 134-135 | DOI

[159] Narayan, N. R.; Weinmaier, T.; Laserna-Mendieta, E. J.; Claesson, M. J.; Shanahan, F.; Dabbagh, K.; Iwai, S.; DeSantis, T. Z. Piphillin predicts metagenomic composition and dynamics from DADA2-corrected 16S rDNA sequences, BMC Genomics, Volume 21 (2020) no. 1 | DOI

[160] Nasko, D. J.; Koren, S.; Phillippy, A. M.; Treangen, T. J. RefSeq database growth influences the accuracy of k-mer-based lowest common ancestor species identification, Genome Biology, Volume 19 (2018) no. 1 | DOI

[161] Nearing, J. T.; Douglas, G. M.; Comeau, A. M.; Langille, M. G. Denoising the Denoisers: an independent evaluation of microbiome sequence error-correction approaches, PeerJ, Volume 6 (2018) | DOI

[162] Nejman, D.; Livyatan, I.; Fuks, G.; Gavert, N.; Zwang, Y.; Geller, L. T.; Rotter-Maskowitz, A.; Weiser, R.; Mallel, G.; Gigi, E.; Meltser, A.; Douglas, G. M.; Kamer, I.; Gopalakrishnan, V.; Dadosh, T.; Levin-Zaidman, S.; Avnet, S.; Atlan, T.; Cooper, Z. A.; Arora, R.; Cogdill, A. P.; Khan, M. A. W.; Ologun, G.; Bussi, Y.; Weinberger, A.; Lotan-Pompan, M.; Golani, O.; Perry, G.; Rokah, M.; Bahar-Shany, K.; Rozeman, E. A.; Blank, C. U.; Ronai, A.; Shaoul, R.; Amit, A.; Dorfman, T.; Kremer, R.; Cohen, Z. R.; Harnof, S.; Siegal, T.; Yehuda-Shnaidman, E.; Gal-Yam, E. N.; Shapira, H.; Baldini, N.; Langille, M. G. I.; Ben-Nun, A.; Kaufman, B.; Nissan, A.; Golan, T.; Dadiani, M.; Levanon, K.; Bar, J.; Yust-Katz, S.; Barshack, I.; Peeper, D. S.; Raz, D. J.; Segal, E.; Wargo, J. A.; Sandbank, J.; Shental, N.; Straussman, R. The human tumor microbiome is composed of tumor type–specific intracellular bacteria, Science, Volume 368 (2020) no. 6494, pp. 973-980 | DOI

[163] NIH A review of 10 years of human microbiome research activities at the US National Institutes of Health, Fiscal Years 2007-2016, Microbiome, Volume 7 (2019) no. 1 | DOI

[164] Ning, J.; Beiko, R. G. Phylogenetic approaches to microbial community classification, Microbiome, Volume 3 (2015) no. 1 | DOI

[165] Nurk, S.; Meleshko, D.; Korobeynikov, A.; Pevzner, P. A. metaSPAdes: a new versatile metagenomic assembler, Genome Research, Volume 27 (2017) no. 5, pp. 824-834 | DOI

[166] Oberhardt, M. A.; Puchałka, J.; Fryer, K. E.; Martins dos Santos, V. A. P.; Papin, J. A. Genome-Scale Metabolic Network Analysis of the Opportunistic Pathogen Pseudomonas aeruginosa PAO1, Journal of Bacteriology, Volume 190 (2008) no. 8, pp. 2790-2803 | DOI

[167] Ogier, J.-C.; Pagès, S.; Galan, M.; Barret, M.; Gaudriault, S. rpoB, a promising marker for analyzing the diversity of bacterial communities by amplicon sequencing, BMC Microbiology, Volume 19 (2019) no. 1 | DOI

[168] Oh, J.; Byrd, A. L.; Deming, C.; Conlan, S.; Kong, H. H.; Segre, J. A. Biogeography and individuality shape function in the human skin metagenome, Nature, Volume 514 (2014) no. 7520, pp. 59-64 | DOI

[169] Olson, N. D.; Treangen, T. J.; Hill, C. M.; Cepeda-Espinoza, V.; Ghurye, J.; Koren, S.; Pop, M. Metagenomic assembly through the lens of validation: recent advances in assessing and improving the quality of genomes assembled from metagenomes, Briefings in Bioinformatics, Volume 20 (2019) no. 4, pp. 1140-1150 | DOI

[170] Omelchenko, M. V.; Galperin, M. Y.; Wolf, Y. I.; Koonin, E. V. Non-homologous isofunctional enzymes: A systematic analysis of alternative solutions in enzyme evolution, Biology Direct, Volume 5 (2010) no. 1 | DOI

[171] Parks, D. H.; Imelfort, M.; Skennerton, C. T.; Hugenholtz, P.; Tyson, G. W. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes, Genome Research, Volume 25 (2015) no. 7, pp. 1043-1055 | DOI

[172] Pasolli, E.; Truong, D. T.; Malik, F.; Waldron, L.; Segata, N. Machine Learning Meta-analysis of Large Metagenomic Datasets: Tools and Biological Insights, PLOS Computational Biology, Volume 12 (2016) no. 7 | DOI

[173] Paulson, J. N.; Stine, O. C.; Bravo, H. C.; Pop, M. Differential abundance analysis for microbial marker-gene surveys, Nature Methods, Volume 10 (2013) no. 12, pp. 1200-1202 | DOI

[174] Pericard, P.; Dufresne, Y.; Couderc, L.; Blanquart, S.; Touzet, H. MATAM: reconstruction of phylogenetic marker genes from short sequencing reads in metagenomes, Bioinformatics, Volume 34 (2018) no. 4, pp. 585-591 | DOI

[175] Pollock, J.; Glendinning, L.; Wisedchanwet, T.; Watson, M. The Madness of Microbiome: Attempting To Find Consensus “Best Practice” for 16S Microbiome Studies, Applied and Environmental Microbiology, Volume 84 (2018) no. 7 | DOI

[176] Popa, O.; Dagan, T. Trends and barriers to lateral gene transfer in prokaryotes, Current Opinion in Microbiology, Volume 14 (2011) no. 5, pp. 615-623 | DOI

[177] Prakash, T.; Taylor, T. D. Functional assignment of metagenomic data: challenges and applications, Briefings in Bioinformatics, Volume 13 (2012) no. 6, pp. 711-727 | DOI

[178] Prodan, A.; Tremaroli, V.; Brolin, H.; Zwinderman, A. H.; Nieuwdorp, M.; Levin, E. Comparing bioinformatic pipelines for microbial 16S rRNA amplicon sequencing, PLOS ONE, Volume 15 (2020) no. 1 | DOI

[179] Punta, M.; Coggill, P. C.; Eberhardt, R. Y.; Mistry, J.; Tate, J.; Boursnell, C.; Pang, N.; Forslund, K.; Ceric, G.; Clements, J.; Heger, A.; Holm, L.; Sonnhammer, E. L. L.; Eddy, S. R.; Bateman, A.; Finn, R. D. The Pfam protein families database, Nucleic Acids Research, Volume 40 (2012) no. D1 | DOI

[180] Quast, C.; Pruesse, E.; Yilmaz, P.; Gerken, J.; Schweer, T.; Yarza, P.; Peplies, J.; Glöckner, F. O. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools, Nucleic Acids Research, Volume 41 (2013) no. D1 | DOI

[181] Rahman, S. F.; Olm, M. R.; Morowitz, M. J.; Banfield, J. F. Machine Learning Leveraging Genomes from Metagenomes Identifies Influential Antibiotic Resistance Genes in the Infant Gut Microbiome, mSystems, Volume 3 (2018) no. 1 | DOI

[182] Rasko, D. A.; Rosovitz, M. J.; Myers, G. S. A.; Mongodin, E. F.; Fricke, W. F.; Gajer, P.; Crabtree, J.; Sebaihia, M.; Thomson, N. R.; Chaudhuri, R.; Henderson, I. R.; Sperandio, V.; Ravel, J. The Pangenome Structure of Escherichia coli : Comparative Genomic Analysis of E. coli Commensal and Pathogenic Isolates, Journal of Bacteriology, Volume 190 (2008) no. 20, pp. 6881-6893 | DOI

[183] Riley, M. Functions of the gene products of Escherichia coli, Microbiological Reviews, Volume 57 (1993) no. 4, pp. 862-952 | DOI

[184] Salonen, A.; Nikkilä, J.; Jalanka-Tuovinen, J.; Immonen, O.; Rajilić-Stojanović, M.; Kekkonen, R. A.; Palva, A.; de Vos, W. M. Comparative analysis of fecal DNA extraction methods with phylogenetic microarray: Effective recovery of bacterial and archaeal DNA using mechanical cell lysis, Journal of Microbiological Methods, Volume 81 (2010) no. 2, pp. 127-134 | DOI

[185] Schloss, P. D. Reintroducing mothur: 10 Years Later, Applied and Environmental Microbiology, Volume 86 (2020) no. 2 | DOI

[186] Schoch, C. L.; Seifert, K. A.; Huhndorf, S.; Robert, V.; Spouge, J. L.; Levesque, C. A.; Chen, W.; Bolchacova, E.; Voigt, K.; Crous, P. W.; Miller, A. N.; Wingfield, M. J.; Aime, M. C.; An, K.-D.; Bai, F.-Y.; Barreto, R. W.; Begerow, D.; Bergeron, M.-J.; Blackwell, M.; Boekhout, T.; Bogale, M.; Boonyuen, N.; Burgaz, A. R.; Buyck, B.; Cai, L.; Cai, Q.; Cardinali, G.; Chaverri, P.; Coppins, B. J.; Crespo, A.; Cubas, P.; Cummings, C.; Damm, U.; de Beer, Z. W.; de Hoog, G. S.; Del-Prado, R.; Dentinger, B.; Dieguez-Uribeondo, J.; Divakar, P. K.; Douglas, B.; Duenas, M.; Duong, T. A.; Eberhardt, U.; Edwards, J. E.; Elshahed, M. S.; Fliegerova, K.; Furtado, M.; Garcia, M. A.; Ge, Z.-W.; Griffith, G. W.; Griffiths, K.; Groenewald, J. Z.; Groenewald, M.; Grube, M.; Gryzenhout, M.; Guo, L.-D.; Hagen, F.; Hambleton, S.; Hamelin, R. C.; Hansen, K.; Harrold, P.; Heller, G.; Herrera, C.; Hirayama, K.; Hirooka, Y.; Ho, H.-M.; Hoffmann, K.; Hofstetter, V.; Hognabba, F.; Hollingsworth, P. M.; Hong, S.-B.; Hosaka, K.; Houbraken, J.; Hughes, K.; Huhtinen, S.; Hyde, K. D.; James, T.; Johnson, E. M.; Johnson, J. E.; Johnston, P. R.; Jones, E. B. G.; Kelly, L. J.; Kirk, P. M.; Knapp, D. G.; Koljalg, U.; Kovacs, G. M.; Kurtzman, C. P.; Landvik, S.; Leavitt, S. D.; Liggenstoffer, A. S.; Liimatainen, K.; Lombard, L.; Luangsa-ard, J. J.; Lumbsch, H. T.; Maganti, H.; Maharachchikumbura, S. S. N.; Martin, M. P.; May, T. W.; McTaggart, A. R.; Methven, A. S.; Meyer, W.; Moncalvo, J.-M.; Mongkolsamrit, S.; Nagy, L. G.; Nilsson, R. H.; Niskanen, T.; Nyilasi, I.; Okada, G.; Okane, I.; Olariaga, I.; Otte, J.; Papp, T.; Park, D.; Petkovits, T.; Pino-Bodas, R.; Quaedvlieg, W.; Raja, H. A.; Redecker, D.; Rintoul, T. L.; Ruibal, C.; Sarmiento-Ramirez, J. M.; Schmitt, I.; Schussler, A.; Shearer, C.; Sotome, K.; Stefani, F. O. P.; Stenroos, S.; Stielow, B.; Stockinger, H.; Suetrong, S.; Suh, S.-O.; Sung, G.-H.; Suzuki, M.; Tanaka, K.; Tedersoo, L.; Telleria, M. T.; Tretter, E.; Untereiner, W. A.; Urbina, H.; Vagvolgyi, C.; Vialle, A.; Vu, T. D.; Walther, G.; Wang, Q.-M.; Wang, Y.; Weir, B. S.; Weiss, M.; White, M. M.; Xu, J.; Yahr, R.; Yang, Z. L.; Yurkov, A.; Zamora, J.-C.; Zhang, N.; Zhuang, W.-Y.; Schindel, D. Nuclear ribosomal internal transcribed spacer (ITS) region as a universal DNA barcode marker for Fungi, Proceedings of the National Academy of Sciences, Volume 109 (2012) no. 16, pp. 6241-6246 | DOI

[187] Schwager, E.; Mallick, H.; Ventz, S.; Huttenhower, C. A Bayesian method for detecting pairwise associations in compositional data, PLOS Computational Biology, Volume 13 (2017) no. 11 | DOI

[188] Segata, N.; Izard, J.; Waldron, L.; Gevers, D.; Miropolsky, L.; Garrett, W. S.; Huttenhower, C. Metagenomic biomarker discovery and explanation, Genome Biology, Volume 12 (2011) no. 6 | DOI

[189] Shade, A. Diversity is the question, not the answer, The ISME Journal, Volume 11 (2017) no. 1, pp. 1-6 | DOI

[190] Shapley, L. S. A value for n-person games In: Contributions to the Theory of Games, 2. Ed. by H W Kuhn and W Tucker. (1953), pp. 307-317

[191] Silverman, J. D.; Washburne, A. D.; Mukherjee, S.; David, L. A. A phylogenetic transform enhances analysis of compositional microbiota data, eLife, Volume 6 (2017) | DOI

[192] Sinha, R.; Abu-Ali, G.; Vogtmann, E.; Fodor, A. A.; Ren, B.; Amir, A.; Schwager, E.; Crabtree, J.; Ma, S.; Abnet, C. C.; Knight, R.; White, O.; Huttenhower, C. Assessment of variation in microbial community amplicon sequencing by the Microbiome Quality Control (MBQC) project consortium, Nature Biotechnology, Volume 35 (2017) no. 11, pp. 1077-1086 | DOI

[193] Spang, A.; Saw, J. H.; Jørgensen, S. L.; Zaremba-Niedzwiedzka, K.; Martijn, J.; Lind, A. E.; van Eijk, R.; Schleper, C.; Guy, L.; Ettema, T. J. G. Complex archaea that bridge the gap between prokaryotes and eukaryotes, Nature, Volume 521 (2015) no. 7551, pp. 173-179 | DOI

[194] Spencer, S. J.; Tamminen, M. V.; Preheim, S. P.; Guo, M. T.; Briggs, A. W.; Brito, I. L.; A Weitz, D.; Pitkänen, L. K.; Vigneault, F.; Virta, M. P.; Alm, E. J. Massively parallel sequencing of single cells by epicPCR links functional genes with phylogenetic markers, The ISME Journal, Volume 10 (2016) no. 2, pp. 427-436 | DOI

[195] Sperling, J. L.; Silva-Brandão, K.; Brandão, M.; Lloyd, V.; Dang, S.; Davis, C.; Sperling, F.; Magor, K. Comparison of bacterial 16S rRNA variable regions for microbiome surveys of ticks, Ticks and Tick-borne Diseases, Volume 8 (2017) no. 4, pp. 453-461 | DOI

[196] STACKEBRANDT, E.; GOEBEL, B. M. Taxonomic Note: A Place for DNA-DNA Reassociation and 16S rRNA Sequence Analysis in the Present Species Definition in Bacteriology, International Journal of Systematic and Evolutionary Microbiology, Volume 44 (1994) no. 4, pp. 846-849 | DOI

[197] Staley, J. T.; Konopka, A. MEASUREMENT OF IN SITU ACTIVITIES OF NONPHOTOSYNTHETIC MICROORGANISMS IN AQUATIC AND TERRESTRIAL HABITATS, Annual Review of Microbiology, Volume 39 (1985) no. 1, pp. 321-346 | DOI

[198] Stein, J. L.; Marsh, T. L.; Wu, K. Y.; Shizuya, H.; DeLong, E. F. Characterization of uncultivated prokaryotes: isolation and analysis of a 40-kilobase-pair genome fragment from a planktonic marine archaeon, Journal of Bacteriology, Volume 178 (1996) no. 3, pp. 591-599 | DOI

[199] Steinegger, M.; Söding, J. MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets, Nature Biotechnology, Volume 35 (2017) no. 11, pp. 1026-1028 | DOI

[200] Steinegger, M.; Söding, J. Clustering huge protein sequence sets in linear time, Nature Communications, Volume 9 (2018) no. 1 | DOI

[201] Suardana, I. W. Analysis of Nucleotide Sequences of the 16S rRNA Gene of NovelEscherichia coliStrains Isolated from Feces of Human and Bali Cattle, Journal of Nucleic Acids, Volume 2014, pp. 1-7 | DOI

[202] Sun, D.-L.; Jiang, X.; Wu, Q. L.; Zhou, N.-Y. Intragenomic Heterogeneity of 16S rRNA Genes Causes Overestimation of Prokaryotic Diversity, Applied and Environmental Microbiology, Volume 79 (2013) no. 19, pp. 5962-5969 | DOI

[203] Sun, S.; Jones, R. B.; Fodor, A. A. Inference-based accuracy of metagenome prediction tools varies across sample types and functional categories, Microbiome, Volume 8 (2020) no. 1 | DOI

[204] Sunagawa, S.; Coelho, L. P.; Chaffron, S.; Kultima, J. R.; Labadie, K.; Salazar, G.; Djahanschiri, B.; Zeller, G.; Mende, D. R.; Alberti, A.; Cornejo-Castillo, F. M.; Costea, P. I.; Cruaud, C.; d'Ovidio, F.; Engelen, S.; Ferrera, I.; Gasol, J. M.; Guidi, L.; Hildebrand, F.; Kokoszka, F.; Lepoivre, C.; Lima-Mendez, G.; Poulain, J.; Poulos, B. T.; Royo-Llonch, M.; Sarmento, H.; Vieira-Silva, S.; Dimier, C.; Picheral, M.; Searson, S.; Kandels-Lewis, S.; Bowler, C.; de Vargas, C.; Gorsky, G.; Grimsley, N.; Hingamp, P.; Iudicone, D.; Jaillon, O.; Not, F.; Ogata, H.; Pesant, S.; Speich, S.; Stemmann, L.; Sullivan, M. B.; Weissenbach, J.; Wincker, P.; Karsenti, E.; Raes, J.; Acinas, S. G.; Bork, P.; Boss, E.; Bowler, C.; Follows, M.; Karp-Boss, L.; Krzic, U.; Reynaud, E. G.; Sardet, C.; Sieracki, M.; Velayoudon, D. Structure and function of the global ocean microbiome, Science, Volume 348 (2015) no. 6237 | DOI

[205] Sze, M. A.; Schloss, P. D. Looking for a Signal in the Noise: Revisiting Obesity and the Microbiome, mBio, Volume 7 (2016) no. 4 | DOI

[206] Tatusov, R. L. The COG database: a tool for genome-scale analysis of protein functions and evolution, Nucleic Acids Research, Volume 28 (2000) no. 1, pp. 33-36 | DOI

[207] Tessler, M.; Neumann, J. S.; Afshinnekoo, E.; Pineda, M.; Hersch, R.; Velho, L. F. M.; Segovia, B. T.; Lansac-Toha, F. A.; Lemke, M.; DeSalle, R.; Mason, C. E.; Brugler, M. R. Large-scale differences in microbial biodiversity discovery between 16S amplicon and shotgun sequencing, Scientific Reports, Volume 7 (2017) no. 1 | DOI

[208] Correction for vol. 102, p. 13954, Proceedings of the National Academy of Sciences, Volume 102 (2005) no. 45, p. 16530-16530 | DOI

[209] Thomas, P. D.; Mi, H.; Lewis, S. Ontology annotation: mapping genomic regions to biological function, Current Opinion in Chemical Biology, Volume 11 (2007) no. 1, pp. 4-11 | DOI

[210] Thompson, L. R.; Sanders, J. G.; McDonald, D.; Amir, A.; Ladau, J.; Locey, K. J.; Prill, R. J.; Tripathi, A.; Gibbons, S. M.; Ackermann, G.; Navas-Molina, J. A.; Janssen, S.; Kopylova, E.; Vázquez-Baeza, Y.; González, A.; Morton, J. T.; Mirarab, S.; Zech Xu, Z.; Jiang, L.; Haroon, M. F.; Kanbar, J.; Zhu, Q.; Jin Song, S.; Kosciolek, T.; Bokulich, N. A.; Lefler, J.; Brislawn, C. J.; Humphrey, G.; Owens, S. M.; Hampton-Marcell, J.; Berg-Lyons, D.; McKenzie, V.; Fierer, N.; Fuhrman, J. A.; Clauset, A.; Stevens, R. L.; Shade, A.; Pollard, K. S.; Goodwin, K. D.; Jansson, J. K.; Gilbert, J. A.; Knight, R. A communal catalogue reveals Earth’s multiscale microbial diversity, Nature, Volume 551 (2017) no. 7681, pp. 457-463 | DOI

[211] Thorsen, J.; Brejnrod, A.; Mortensen, M.; Rasmussen, M. A.; Stokholm, J.; Al-Soud, W. A.; Sørensen, S.; Bisgaard, H.; Waage, J. Large-scale benchmarking reveals false discoveries and count transformation sensitivity in 16S rRNA gene amplicon data analysis methods used in microbiome studies, Microbiome, Volume 4 (2016) no. 1 | DOI

[212] Treem, W. R.; Ahsan, N.; Shoup, M.; Hyams, J. S. Fecal Short-Chain Fatty Acids in Children with Inflammatory Bowel Disease, Journal of Pediatric Gastroenterology and Nutrition, Volume 18 (1994) no. 2, pp. 159-164 | DOI

[213] Truong, D. T.; Franzosa, E. A.; Tickle, T. L.; Scholz, M.; Weingart, G.; Pasolli, E.; Tett, A.; Huttenhower, C.; Segata, N. MetaPhlAn2 for enhanced metagenomic taxonomic profiling, Nature Methods, Volume 12 (2015) no. 10, pp. 902-903 | DOI

[214] Turnbaugh, P. J.; Bäckhed, F.; Fulton, L.; Gordon, J. I. Diet-Induced Obesity Is Linked to Marked but Reversible Alterations in the Mouse Distal Gut Microbiome, Cell Host & Microbe, Volume 3 (2009) no. 4, pp. 213-223 | DOI

[215] Turnbaugh, P. J.; Hamady, M.; Yatsunenko, T.; Cantarel, B. L.; Duncan, A.; Ley, R. E.; Sogin, M. L.; Jones, W. J.; Roe, B. A.; Affourtit, J. P.; Egholm, M.; Henrissat, B.; Heath, A. C.; Knight, R.; Gordon, J. I. A core gut microbiome in obese and lean twins, Nature, Volume 457 (2008) no. 7228, pp. 480-484 | DOI

[216] Vandeputte, D.; Kathagen, G.; D’hoe, K.; Vieira-Silva, S.; Valles-Colomer, M.; Sabino, J.; Wang, J.; Tito, R. Y.; De Commer, L.; Darzi, Y.; Vermeire, S.; Falony, G.; Raes, J. Quantitative microbiome profiling links gut community variation to microbial load, Nature, Volume 551 (2017) no. 7681, pp. 507-511 | DOI

[217] Vatanen, T.; Plichta, D. R.; Somani, J.; Münch, P. C.; Arthur, T. D.; Hall, A. B.; Rudolf, S.; Oakeley, E. J.; Ke, X.; Young, R. A.; Haiser, H. J.; Kolde, R.; Yassour, M.; Luopajärvi, K.; Siljander, H.; Virtanen, S. M.; Ilonen, J.; Uibo, R.; Tillmann, V.; Mokurov, S.; Dorshakova, N.; Porter, J. A.; McHardy, A. C.; Lähdesmäki, H.; Vlamakis, H.; Huttenhower, C.; Knip, M.; Xavier, R. J. Genomic variation and strain-specific functional adaptation in the human gut microbiome during early life, Nature Microbiology, Volume 4 (2018) no. 3, pp. 470-479 | DOI

[218] Parada Venegas, D.; De la Fuente, M. K.; Landskron, G.; González, M. J.; Quera, R.; Dijkstra, G.; Harmsen, H. J. M.; Faber, K. N.; Hermoso, M. A. Short Chain Fatty Acids (SCFAs)-Mediated Gut Epithelial and Immune Regulation and Its Relevance for Inflammatory Bowel Diseases, Frontiers in Immunology, Volume 10 (2019) | DOI

[219] Venter, J. C.; Remington, K.; Heidelberg, J. F.; Halpern, A. L.; Rusch, D.; Eisen, J. A.; Wu, D.; Paulsen, I.; Nelson, K. E.; Nelson, W.; Fouts, D. E.; Levy, S.; Knap, A. H.; Lomas, M. W.; Nealson, K.; White, O.; Peterson, J.; Hoffman, J.; Parsons, R.; Baden-Tillson, H.; Pfannkoch, C.; Rogers, Y.-H.; Smith, H. O. Environmental Genome Shotgun Sequencing of the Sargasso Sea, Science, Volume 304 (2004) no. 5667, pp. 66-74 | DOI

[220] Verster, A. J.; Borenstein, E. Competitive lottery-based assembly of selected clades in the human gut microbiome, Microbiome, Volume 6 (2018) no. 1 | DOI

[221] Větrovský, T.; Baldrian, P. The Variability of the 16S rRNA Gene in Bacterial Genomes and Its Consequences for Bacterial Community Analyses, PLoS ONE, Volume 8 (2013) no. 2 | DOI

[222] Vincent, C.; Stephens, D. A.; Loo, V. G.; Edens, T. J.; Behr, M. A.; Dewar, K.; Manges, A. R. Reductions in intestinal Clostridiales precede the development of nosocomial Clostridium difficile infection, Microbiome, Volume 1 (2013) no. 1 | DOI

[223] Wang, Y.; Qian, P.-Y. Conserved Regions in 16S Ribosome RNA Sequences and Primer Design for Studies of Environmental Microbes, Encyclopedia of Metagenomics, Springer New York, New York, NY, 2014, pp. 1-6 | DOI

[224] Watson, E.-J.; Giles, J.; Scherer, B. L.; Blatchford, P. Human faecal collection methods demonstrate a bias in microbiome composition by cell wall structure, Scientific Reports, Volume 9 (2019) no. 1 | DOI

[225] Watterson, W. J.; Tanyeri, M.; Watson, A. R.; Cham, C. M.; Shan, Y.; Chang, E. B.; Eren, A. M.; Tay, S. Droplet-based high-throughput cultivation for accurate screening of antibiotic resistant gut microbes, eLife, Volume 9 (2020) | DOI

[226] Weiss, S.; Xu, Z. Z.; Peddada, S.; Amir, A.; Bittinger, K.; Gonzalez, A.; Lozupone, C.; Zaneveld, J. R.; Vázquez-Baeza, Y.; Birmingham, A.; Hyde, E. R.; Knight, R. Normalization and microbial differential abundance strategies depend upon data characteristics, Microbiome, Volume 5 (2017) no. 1 | DOI

[227] Welch, R. A.; Burland, V.; Plunkett, G.; Redford, P.; Roesch, P.; Rasko, D.; Buckles, E. L.; Liou, S. R.; Boutin, A.; Hackett, J.; Stroud, D.; Mayhew, G. F.; Rose, D. J.; Zhou, S.; Schwartz, D. C.; Perna, N. T.; Mobley, H. L. T.; Donnenberg, M. S.; Blattner, F. R. Extensive mosaic structure revealed by the complete genome sequence of uropathogenic Escherichia coli, Proceedings of the National Academy of Sciences, Volume 99 (2002) no. 26, pp. 17020-17024 | DOI

[228] Wemheuer, F.; Taylor, J. A.; Daniel, R.; Johnston, E.; Meinicke, P.; Thomas, T.; Wemheuer, B. Tax4Fun2: prediction of habitat-specific functional profiles and functional redundancy based on 16S rRNA gene sequences, Environmental Microbiome, Volume 15 (2020) no. 1 | DOI

[229] Wheeler, N. E.; Barquist, L.; Kingsley, R. A.; Gardner, P. P. A profile-based method for identifying functional divergence of orthologous genes in bacterial genomes, Bioinformatics (2016) | DOI

[230] Willis, A. D. Rigorous Statistical Methods for Rigorous Microbiome Science, mSystems, Volume 4 (2019) no. 3 | DOI

[231] Willis, C.; Desai, D.; LaRoche, J. Influence of 16S rRNA variable region on perceived diversity of marine microbial communities of the Northern North Atlantic, FEMS Microbiology Letters, Volume 366 (2019) no. 13 | DOI

[232] Wilson, G. A.; Bertrand, N.; Patel, Y.; Hughes, J. B.; Feil, E. J.; Field, D. Orphans as taxonomically restricted and ecologically important genes, Microbiology, Volume 151 (2005) no. 8, pp. 2499-2501 | DOI

[233] Wirbel, J.; Pyl, P. T.; Kartal, E.; Zych, K.; Kashani, A.; Milanese, A.; Fleck, J. S.; Voigt, A. Y.; Palleja, A.; Ponnudurai, R.; Sunagawa, S.; Coelho, L. P.; Schrotz-King, P.; Vogtmann, E.; Habermann, N.; Niméus, E.; Thomas, A. M.; Manghi, P.; Gandini, S.; Serrano, D.; Mizutani, S.; Shiroma, H.; Shiba, S.; Shibata, T.; Yachida, S.; Yamada, T.; Waldron, L.; Naccarati, A.; Segata, N.; Sinha, R.; Ulrich, C. M.; Brenner, H.; Arumugam, M.; Bork, P.; Zeller, G. Meta-analysis of fecal metagenomes reveals global microbial signatures that are specific for colorectal cancer, Nature Medicine, Volume 25 (2019) no. 4, pp. 679-689 | DOI

[234] Woese, C. R. Bacterial evolution, Microbiological Reviews, Volume 51 (1987) no. 2, pp. 221-271 | DOI

[235] Woese, C.; Magrum, L.; Gupta, R.; Siegel, R.; Stahl, D.; Kop, J.; Crawford, N.; Brosius, R.; Gutell, R.; Hogan, J.; Noller, H. Secondary structure model for bacterial 16S ribosomal RNA: phylogenetic, enzymatic and chemical evidence, Nucleic Acids Research, Volume 8 (1980) no. 10, pp. 2275-2294 | DOI

[236] Woese, C. R.; Fox, G. E. Phylogenetic structure of the prokaryotic domain: The primary kingdoms, Proceedings of the National Academy of Sciences, Volume 74 (1977) no. 11, pp. 5088-5090 | DOI

[237] Wood, D. E.; Lu, J.; Langmead, B. Improved metagenomic analysis with Kraken 2, Genome Biology, Volume 20 (2019) no. 1 | DOI

[238] Wright, E. K.; Kamm, M. A.; Teo, S. M.; Inouye, M.; Wagner, J.; Kirkwood, C. D. Recent Advances in Characterizing the Gastrointestinal Microbiome in Crohnʼs Disease, Inflammatory Bowel Diseases, Volume 21 (2015) | DOI

[239] Wu, D.; Jospin, G.; Eisen, J. A. Systematic Identification of Gene Families for Use as “Markers” for Phylogenetic and Phylogeny-Driven Ecological Studies of Bacteria and Archaea and Their Major Subgroups, PLoS ONE, Volume 8 (2013) no. 10 | DOI

[240] Xu, Y.; Zhao, F. Single-cell metagenomics: challenges and applications, Protein & Cell, Volume 9 (2018) no. 5, pp. 501-510 | DOI

[241] Ye, S. H.; Siddle, K. J.; Park, D. J.; Sabeti, P. C. Benchmarking Metagenomics Tools for Taxonomic Classification, Cell, Volume 178 (2019) no. 4, pp. 779-794 | DOI

[242] Ye, Y.; Doak, T. G. A Parsimony Approach to Biological Pathway Reconstruction/Inference for Metagenomes, Handbook of Molecular Microbial Ecology I, John Wiley & Sons, Inc., Hoboken, NJ, USA, 2011, pp. 453-460 | DOI

[243] Zaneveld, J. R.; Lozupone, C.; Gordon, J. I.; Knight, R. Ribosomal RNA diversity predicts genome diversity in gut bacteria and their relatives, Nucleic Acids Research, Volume 38 (2010) no. 12, pp. 3869-3879 | DOI

[244] Zaneveld, J. R. R.; Thurber, R. L. V. Hidden state prediction: a modification of classic ancestral state reconstruction algorithms helps unravel complex symbioses, Frontiers in Microbiology, Volume 5 (2014) | DOI

[245] Zemb, O.; Achard, C. S.; Hamelin, J.; De Almeida, M.; Gabinaud, B.; Cauquil, L.; Verschuren, L. M.; Godon, J. Absolute quantitation of microbes using 16S rRNA gene metabarcoding: A rapid normalization of relative abundances by quantitative PCR targeting a 16S rRNA gene spike‐in standard, MicrobiologyOpen, Volume 9 (2020) no. 3 | DOI

[246] Zhou, J.; He, Z.; Yang, Y.; Deng, Y.; Tringe, S. G.; Alvarez-Cohen, L. High-Throughput Metagenomic Technologies for Complex Microbial Community Analysis: Open and Closed Formats, mBio, Volume 6 (2015) no. 1 | DOI

[247] Zhou, W.; Sailani, M. R.; Contrepois, K.; Zhou, Y.; Ahadi, S.; Leopold, S. R.; Zhang, M. J.; Rao, V.; Avina, M.; Mishra, T.; Johnson, J.; Lee-McMullen, B.; Chen, S.; Metwally, A. A.; Tran, T. D. B.; Nguyen, H.; Zhou, X.; Albright, B.; Hong, B.-Y.; Petersen, L.; Bautista, E.; Hanson, B.; Chen, L.; Spakowicz, D.; Bahmani, A.; Salins, D.; Leopold, B.; Ashland, M.; Dagan-Rosenfeld, O.; Rego, S.; Limcaoco, P.; Colbert, E.; Allister, C.; Perelman, D.; Craig, C.; Wei, E.; Chaib, H.; Hornburg, D.; Dunn, J.; Liang, L.; Rose, S. M. S.-F.; Kukurba, K.; Piening, B.; Rost, H.; Tse, D.; McLaughlin, T.; Sodergren, E.; Weinstock, G. M.; Snyder, M. Longitudinal multi-omics of host–microbe dynamics in prediabetes, Nature, Volume 569 (2019) no. 7758, pp. 663-671 | DOI

[248] Zhou, Y.-H.; Gallins, P. A Review and Tutorial of Machine Learning Methods for Microbiome Host Trait Prediction, Frontiers in Genetics, Volume 10 (2019) | DOI

[249] Zuckerkandl, E.; Pauling, L. Molecules as documents of evolutionary history, Journal of Theoretical Biology, Volume 8 (1965) no. 2, pp. 357-366 | DOI

[250] Bradley, P. H.; Nayfach, S.; Pollard, K. S. Phylogeny-corrected identification of microbial gene families relevant to human gut colonization, PLOS Computational Biology, Volume 14 (2018) no. 8 | DOI

[251] Doolittle, W. F.; Booth, A. It’s the song, not the singer: an exploration of holobiosis and evolutionary theory, Biology & Philosophy, Volume 32 (2017) no. 1, pp. 5-24 | DOI

[252] Lloyd-Price, J.; Arze, C.; Ananthakrishnan, A. N.; Schirmer, M.; Avila-Pacheco, J.; Poon, T. W.; Andrews, E.; Ajami, N. J.; Bonham, K. S.; Brislawn, C. J.; Casero, D.; Courtney, H.; Gonzalez, A.; Graeber, T. G.; Hall, A. B.; Lake, K.; Landers, C. J.; Mallick, H.; Plichta, D. R.; Prasad, M.; Rahnavard, G.; Sauk, J.; Shungin, D.; Vázquez-Baeza, Y.; White, R. A.; Braun, J.; Denson, L. A.; Jansson, J. K.; Knight, R.; Kugathasan, S.; McGovern, D. P. B.; Petrosino, J. F.; Stappenbeck, T. S.; Winter, H. S.; Clish, C. B.; Franzosa, E. A.; Vlamakis, H.; Xavier, R. J.; Huttenhower, C. Multi-omics of the gut microbial ecosystem in inflammatory bowel diseases, Nature, Volume 569 (2019) no. 7758, pp. 655-662 | DOI

[253] Louca, S.; Polz, M. F.; Mazel, F.; Albright, M. B. N.; Huber, J. A.; O’Connor, M. I.; Ackermann, M.; Hahn, A. S.; Srivastava, D. S.; Crowe, S. A.; Doebeli, M.; Parfrey, L. W. Function and functional redundancy in microbial systems, Nature Ecology & Evolution, Volume 2 (2018) no. 6, pp. 936-943 | DOI

Cited by Sources: